DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The invention relates to a system and method for using econometric techniques to quantify the marketing drivers of consumer demand, retail-load adjustments, and shipments. The retail-load adjustments may be retail demand for a product or service generated by future sales incentives, new product launches or other factors which motivate retailers to carry extra stock.

[0013] According to one embodiment of the invention, a marketing plan that includes at least one marketing element may be provided by a business, step 100. The marketing element may be, for example, promotions, advertising, points of distribution, product changes, etc. Historical marketing spend data and historical sales data may be gathered to determine the correlation of past marketing activities on sales and retail load adjustments, step 102. Preferably, data is collected for at least the past twenty-four (24) reporting periods (e.g., months). Regression analysis or other econometric modeling techniques may be performed to calculate the effect of marketing variables on consumer demand and retail load adjustments, step 104.

[0014] A lift coefficient may be assigned to each variable and may be used to forecast future consumer demand, anticipated retail load adjustments, and total shipments. The total shipments may be defined as a sum of consumer demand and anticipated retail-load adjustments. The marketing forecasting tool enables users to enter planned marketing spend amounts for each marketing variable to forecast consumer demand and required shipments, step 106.

[0015] According to one embodiment, regression analysis or other econometric modeling techniques may be used to directly forecast shipments based on marketing spending rather than modeling the effects on consumer demand and retail load adjustments to build up the shipment forecast. In these instances, econometric modeling techniques may be used to analyze historical marketing spend data and historical shipments to calculate the quantifiable impact of each marketing element directly on shipments.

[0016] Econometric modeling has been used by others to explain why certain circumstances have occurred. However, the present invention now uses econometric modeling as a forecasting tool—looking forward rather than backward. Shipment data has also be leveraged by others for various purposes. However, prior uses of shipment data has used exponential smoothing and other techniques. The present invention now uses econometric modeling on shipment data since it can show how business decisions drive changes in shipments beyond standard sales activities. One embodiment of the invention is able to forecast approximately two months into the future. A second embodiment is able to forecast a full year into the future. When forecasting on an annual basis, other various factors may be used as part of the econometric modeling step, where such factors are particularly well suited for such longer term forecasting.

[0017] According to one embodiment of the invention, the business may evaluate what-if scenarios and adjust the planned spend on various marketing elements based upon their impact on the forecasts, step 108. For example, the marketing plan for a particular product may include one-hundred-thousand dollars ($100,000) on coupons delivered via electronic mail, five-hundred-thousand dollars ($500,000) on advertising, and two-hundred-fifty-thousand ($250,000) on promotions. The tool may be used to determine what effect on consumer demand and required shipments may result if changes are made to any of the marketing spend values. For example, performing what-if analysis using the tool may indicate that promotions have the largest impact on consumer demand and shipments whereby a one (1) percent increase in spending on promotions may increase demand for the product by twelve (12) percent. Using what-if analysis, the business may decide how to shift more dollars in the marketing plan to promotional spending from the other marketing elements.

[0018] The marketing plan may be executed, step 110. The resulting consumer demand and shipment data may be captured and entered into the tool to determine the accuracy of the forecast, step 112. This enables the forecasted demand and actual demand to be tracked and compared. The results may provide a percentage by which the consumer demand and shipments were forecasted above or below actual demand.

[0019] The tool may be used to enable a user to perform an assessment to identify the reasons for any variances between the consumer demand and shipment forecasts and the actual results, step 114. For example, it may be found that while the marketing plan called for $100,000 to be spent on coupons, only $50,000 was spent resulting in reduced customer demand. The tool may enable a user to enter explanations for any of the variances, step 116. The explanations for the variances and the forecast errors may be logged, step 118. A report that indicates the forecasted demand, actual demand, and explanation for variances may be generated, step 120. A manager or other business personnel may analyze the report to determine whether operational changes are required to generate future results that better resemble the forecast.

[0020] FIG. 2 illustrates a system 200 for analyzing marketing activity effects on shipments according to one embodiment of the invention. Oftentimes, shipments may be indicated as a function of consumer demand and retail-load adjustments. The system 200 may include a marketing plan providing module 202. The marketing plan providing module 202 may be used to provide one or more marketing plans. The marketing plans may include delivering marketing offers to one or more customers via one or more marketing elements. A historical data gathering module 204 may be used to gather historical marketing spend data and sales data to determine a correlation of past marketing activities on sales and retail-load adjustments. The historical data may be analyzed using historical data analyzing module 206. The historical data analyzing module 206 preferably applies various econometric modeling techniques to calculate the effect of marketing variables that represent marketing elements.

[0021] A lift coefficient may be associated with each marketing variable and may be used to forecast future consumer demand, retail load adjustments, and thus, total shipments. In some cases, the lift coefficients may directly correlate the marketing spending to the total shipments as described above.

[0022] The system 200 may enable the user to input how much money may be spent on particular marketing elements of the marketing plan using a user input enabling module 208 as shown in FIG. 3. FIG. 3 is a representation of an input screen that may be used for detailing forecasted demand and shipments and assessing and tracking forecast variances. According to one embodiment of the invention, a user may execute what-if scenarios to evaluate the effect on consumer demand and required shipments when changes are made to the planned spend on particular marketing elements in the marketing plan. A marketing plan modifying module 210 may be used to modify the marketing plan based upon the results of the what-if analysis.

[0023] A marketing plan executing module 212 may be used to execute the marketing plan. This may include, for example, delivering one or more marketing offers to consumers via regular mail, electronic mail, facsimile, telephone call, etc. A marketing plan data results inputting module 214 may be used to input the consumer demand and shipment data into the system 200 to determine the accuracy of the forecast. This enables the forecasted demand and actual demand to be compared. The accuracy of the forecast may be indicated by a percentage by which the consumer demand and shipments were forecasted above or below the actual demand. A reasons assessing module 216 may be used to perform an assessment and to identify reasons for any differences between forecasted and actual consumer demand and shipments.

[0024] The system 200 may also be used to enable a user to enter explanations regarding any of the differences using a forecast variance explanation input enabling module 218. The explanations for the forecast variances as well as the forecast errors may be logged using a forecast error logging module 220. A report that indicates the forecasted demand, actual demand, and explanation for variances may be generated using a report generating module 222. A manager or other business personnel may analyze the report to determine whether operational changes are required to generate future results that better resemble the forecast.

[0025] While the specification describes particular embodiments of the present invention, those of ordinary skill can devise variations of the present invention without departing from the inventive concept.